DocumentCode :
2746066
Title :
Feature extraction using fuzzy complete linear discriminant analysis
Author :
Cui, Yan ; Jin, Zhong
Author_Institution :
Sch. of Comput. Sci. & Technol., Nanjing Univ. of Sci. & Technol., Nanjing, China
fYear :
2012
fDate :
10-15 June 2012
Firstpage :
1
Lastpage :
5
Abstract :
In pattern recognition, feature extraction techniques are widely employed to dimensionality reduction. In this paper, a novel feature extraction method, fuzzy complete linear discriminant analysis (Fuzzy-CLDA), is proposed by combining the complete linear discriminant analysis (CLDA) and the membership degrees of samples. Furthermore, we calculate the sample membership degrees with different distance metrics and compare the effectiveness of the distance metrics. In addition, experiments are provided for analyzing and illustrating our results.
Keywords :
fuzzy set theory; pattern recognition; dimensionality reduction; distance metrics; feature extraction; fuzzy complete linear discriminant analysis; fuzzy-CLDA; pattern recognition; Error analysis; Feature extraction; Linear discriminant analysis; Measurement; Pattern recognition; Principal component analysis; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on
Conference_Location :
Brisbane, QLD
ISSN :
1098-7584
Print_ISBN :
978-1-4673-1507-4
Electronic_ISBN :
1098-7584
Type :
conf
DOI :
10.1109/FUZZ-IEEE.2012.6250813
Filename :
6250813
Link To Document :
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